Towards an early warning system for sovereign defaults leveraging on machine learning methodologies

In this study, we address the topic of credit risk stemming from central governments from a technical point of view. First, we explore various econometric and machine learning techniques to build an enhanced sovereign rating system that effectively differentiates the risk of default among countries. Our empirical results indicate that the machine learning method of XGBOOST has a superior out-of-sample and out-of-time predictive performance. Then, we use the models developed to calibrate a sovereign rating system and provide useful insights into the set-up of a parsimonious early warning system. Our results provide a more concise view of the most robust method for classifying countries’ default risk with significant regulatory implications, given that the efficient assessment of sovereign debt is crucial for effective proactive risk measurement.     

Machine learning as an early warning system to predict financial crisis

This paper studies on “Early Warning Systems” (EWS) by investigating possible contagion risks, based on structured financial networks. Early warning indicators improve standard crisis prediction models performance. Using network analysis and machine learning algorithms we find evidence of contagion risk on the dates where we observe significant increase in correlations and centralities. The effectiveness of machine learning reached 98.8%, making the predictions extremely accurate. The model provides significant information to policymakers and investors about employing the financial network as a useful tool to improve portfolio selection by targeting assets based on centrality.     

SAFE: An early warning system for systemic banking risk

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk that incorporates the structural characteristics of the financial system and a feedback amplification mechanism. The models explain financial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifications that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing and policy.     

An early warning indicator system to monitor the unsecured interbank funds market

A core goal of regulators and financial authorities is to understand how market prices convey information on the financial health of its participants. From this viewpoint we build an Early-Warning Indicators System (EWIS) that allows for identifying those financial institutions perceived as risky counterparts by the participants of the interbank market. We use micro-level data from bilateral overnight unsecured loans performed in the interbank market between January 2011 and December 2014. The EWIS identifies those participants that systematically pay high prices for liquidity in this market. We employ coverage tests to estimate EWIS’ robustness and consistency. We find that financial institutions with an elevated frequency of signals tend to exhibit a net borrower liquidity position in the interbank market, hence suggesting they are facing recurrent liquidity needs. Those institutions also exhibit higher probability of insolvency measured by the Z-score indicator. Thus, our results support the existence of market discipline based on peer-monitoring. Overall, the EWIS may assist financial authorities in focusing their attention and resources on those financial institutions perceived by the market as those closer to distress.     

The ADR shadow exchange rate as an early warning indicator for currency crises

We develop an indicator for currency crisis risk using price spreads between American Depositary Receipts (ADRs) and their underlyings. This risk measure represents the mean exchange rate ADR investors expect after a potential currency crisis or realignment. It makes crisis prediction possible on a daily basis as depreciation expectations are reflected in ADR market prices. Using daily data, we analyze the impact of several risk drivers related to standard currency crisis theories and find that ADR investors perceive higher currency crisis risk when export commodity prices fall, trading partners’ currencies depreciate, sovereign yield spreads increase, or interest rate spreads widen.     

Political images of the future: The Dutch case

The Netherlands Scientific Council for Government Policy is responsible for supplying, for use in deciding government policy, scientifically sound information on developments which may affect society in the long term. It also draws attention to anticipated anomalies and bottlenecks, defines major policy problems, and indicates alternatives. This article describes the Council's future survey and its use of various normative perceptions discernible in the political system. Six characteristic sociopolitical orientations are identified and the likely consequences of each are depicted, with the goal of stimulating future-oriented policies. Finally, the Dutch approach is compared with the approaches in other surveys of the future.     

Using signposts in future studies: Lessons from the Justice for tomorrow project

Within the field of future studies, the scenario method is frequently applied. In the literature it is often stressed that it is important to know as soon as possible which of several scenarios is closest to the course of history as it actually unfolds. However, tracking scenarios via early warning mechanisms or signposts, is not a common practice. A standard methodology seems to be absent. Within the context of the Justice for tomorrow project, a scenario project of the Dutch ministry of Justice, we developed and applied a signpost method. We used this method to answer the question of how actual developments relate to the development paths depicted in the scenarios. In this paper we evaluate our approach. We explain what lessons can be learned regarding the use of signposts in future studies.     

Research on optimization of an enterprise financial risk early warning method based on the DS-RF model

The financial risk early warning process of enterprises faces problems such as uncertainty and complexity. In the big data environment, scholars and enterprises that continue to use traditional evaluation methods will face large challenges. It is essential for an enterprise's sustainable operation to combine artificial intelligence algorithms, dynamically monitor its financial risks, and carry out financial risk early warning processes accurately and effectively. This study proposes an early warning method for corporate financial risks based on the evidence theory-random forest (DS-RF) model. The classic algorithm of machine learning—random forest was introduced into the framework of evidence theory to construct a random forest model with four dimensions: profitability, asset quality, debt risk, and operating growth. While predicting the risk, the credibility of the evidence was determined, and then the D-S synthesis rule was used for information fusion. An example was analyzed, taking JS Reclamation Group as the study subject. The comparison with the early warning results of the random forest algorithm and the traditional model shows that the DS-RF model proposed in this paper has a higher early warning accuracy and the results are presented more comprehensively and systematically, which effectively improves the efficiency of enterprise financial risk early warning and helps managers to make relevant decisions efficiently and scientifically.     

Building the future: Undertaking proactive strategy for national outlook

Future studies should provide the means to forecast potential crisis in order to avoid them or limit their effects [Masini, E. 2006, Rethinking futures studies, Futures 38, 1158-1168]. Recognizing and realizing the future events from past up to the present have been the main concern of the rulers and policy makers from the very beginning of civilization. Future studies are a decisive part of the public decision process which plays a very distinctive role in the desirable future of a society. The role of elites in future studies and scenario building is notable; they design national policies and strategies and have a significant impact in general public. Although future cannot be foretold by anyone, the interactions of advanced nations seem to be counter productive; they try to depict the futures. In this case, the futures are built, not predicted. The interactions of two or more systems at the level of international relations have been discussed in this paper. The imaginations of general public toward the futures are given in each society by using these analogies: railroad, river, sea and game. Therefore, the intervention of advanced nations may confront passive, harmonic, active, proactive and chaotic reaction in the other nations. We have discussed that undertaking proactive strategy is the best way to design national perspective.     

Management of catastrophic risks considering the existence of early warning systems

It is crucially important to incorporate the notion of early warning systems in insurance mathematics. We develop the theory of an arrival process taking into account an early warning system, and we use it to create appropriate actuarial models. Then, we formulate a stochastic optimization problem to find an investment strategy for the management of a fund from the perspective of a risk-averse government. The solution is given using the Föllmer-Schweizer strategy.     

银行综合业务风险预警系统的设计

多年以来,我行会计业务稽核工作一直沿用对会计和储蓄业务进行事后集中稽核的方式,即通过再次录入凭证,对流水账进行核实,以起到控制操作风险的作用.随着业务的快速发展,特别是总行数据集中工程的启动,这种延续多年的管理和稽核模式已不能适应我行业务发展的需要.如何依靠先进的技术手段,采用先进的管理办法,改革原有的稽核方式,切实提升内控管理能力,已成为我们面临的一项非常重要的工作.     

关于构建基层央行廉政预警监测机制的探讨

建立科学的廉政预警监测机制,及时全面掌握有关廉政工作信息,对获取的廉政信息梳理归纳,进行定量定性分析,能够从中发现存在的漏洞和薄弱环节,正确分析和判断腐败行为的苗头和动向,研究解决问题的相关措施,对反腐败工作深入开展具有重要意义。本文结合基层央行纪检监察工作实际,提出了如何构建基层央行廉政预警监测系统基本框架和指标体系的思路,以及进一步提升基层央行廉政预警监测水平的具体建议。     

Reinventing the Dutch tax-benefit system: exploring the frontier of the equity-efficiency trade-off

European governments aim to raise labour supply, cut unemployment and, at the same time, maintain social cohesion. Yet, economists have stressed the trade-off between these objectives. This paper reviews the key policy insights from optimal tax theory to identify options for reform in the tax-benefit system that can potentially improve the equity-efficiency trade-off. Using a comprehensive applied general equilibrium model, we then explore whether reforms along these lines in the Dutch tax-benefit system raise employment without sacrificing equality. The analysis reveals that selective tax relief for elastic secondary earners and low-skilled workers have this potential. A flat income tax structure, possibly combined with a negative income tax, worsens the equity-efficiency trade-off.      

Gaming the future of an urban network

Scenario and gaming techniques have a number of complementary characteristics. In this article, the design and evaluation of a gaming-scenario experiment for the exploration of development planning in an urban network in the Netherlands is presented. Two gaming sessions were held using two long-term scenarios (2030) as varying contexts. The combined gaming-scenario approach made it possible to evaluate the impact of external future developments and trends on the administrative and spatial development. Evaluation results of the gaming experiment indicate that the gaming-scenario approach generated new and critical insights on development planning and the future of the urban network. Through the game, the principal, the game leaders but most of all the stakeholder-participants experienced and realized that the management of development planning in urban networks is a very difficult task and is full of pitfalls.     

构建我国货币危机预警机制--基于KLR信号法的研究

当前建立我国货币危机预警机制是必要的也是可行的。KLR信号法对建立货币危机预警系统具有特殊的应用价值。KLR信号法还存在许多不足,应加以改进。     

浅议债券交易类帐户考核指标体系的设立

该文认为目前国内金融机构债券交易类帐户设置的考核指标体系普遍存在不全面或者不合理的问题;外资金融机构债券交易类帐户的考核方法虽然有值得借鉴的地方,但仍需要结合我国的实际情况辩证地吸收,才能制定出适合国情而又合理有效的考核方法。最后文章针对不同类型、不同考核要求的金融机构分别提出相应可行的考核办法。     

农村信用社金融风险预警系统设计

近年来,农村信用社的管理体制和组织形式进行了重要的改革,农村信用社的经营也保持了较快速度的增长.需要立足于农村信用社自身,尽快建立能够迅速反映农信社体制性和经营性风险状况,适合其营运特点的风险预警系统.本文依据风险管理理论,利用原型和面向对象的系统开发方法、结构化的程序设计方法,在网络环境下设计具有风险警情判断和风险原因诊断功能的农信社运行风险预警系统.具体包括数据维护、参数确定、风险分析、警情判断、警情诊断等功能.     

构建银行网络协作式安全体系

为了适应现代银行发展的趋势,银行业通过金融信息化建设,在改进服务手段、增加服务功能、完善业务品种和提高服务效率等方面做了大量卓有成效的工作,提高了银行的核心竞争力,获得了很好的经济效益。其中,联接全行分支机构的网络是银行的“信息动脉”和“信息高速公路”,网络的安全和畅通,直接影响银行金融服务方式和服务质量,因此网络安全建设是金融信息化的重要主题之一。     

构建国际一流信息技术体系打造国际顶级金融保险集团——访中国人寿保险（集团）公司信息技术部总经理朱宏玲

改革开放30多年来,伴随中国金融业的快速发展,中国人寿保险（集团）公司（以下简称“中国人寿”）也取得了令人瞩目的发展成就,成为目前中国最大的商业保险集团,总资产超过一万亿元,连续六年入选《财富》全球500强,连续两年入选“世界品牌500强”。与集团公司发展同步,中国人寿的信息化工作走过了一条不平凡的发展之路,在IT治理体系、应用体系、